Evidence map›Paper›PMID 33719329›Full record

ArticleFrontiers in genetics2021

Gene Set Enrichment Analysis of Interaction Networks Weighted by Node Centrality.

Alessandra Zito, Marta Lualdi, Paola Granata, Dario Cocciadiferro, Antonio Novelli, Tiziana Alberio, Rosario Casalone, Mauro Fasano

Abstract read
In one paragraph

Article in Frontiers in genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

32 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Alessandra ZitoDepartment of Science and High Technology, Center of Bioinformatics, University of Insubria, Busto Arsizio, Italy.
Marta LualdiDepartment of Science and High Technology, Center of Bioinformatics, University of Insubria, Busto Arsizio, Italy.
Paola GranataUnit of Cytogenetics and Medical Genetics, ASST dei Sette Laghi, Varese, Italy.
Dario CocciadiferroLaboratory of Medical Genetics, Ospedale Pediatrico Bambino Gesù, Rome, Italy.
Antonio NovelliLaboratory of Medical Genetics, Ospedale Pediatrico Bambino Gesù, Rome, Italy.
Tiziana AlberioDepartment of Science and High Technology, Center of Bioinformatics, University of Insubria, Busto Arsizio, Italy.
Rosario CasaloneUnit of Cytogenetics and Medical Genetics, ASST dei Sette Laghi, Varese, Italy.
Mauro FasanoDepartment of Science and High Technology, Center of Bioinformatics, University of Insubria, Busto Arsizio, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Gene set enrichment analysis (GSEA) is a powerful tool to associate a disease phenotype to a group of genes/proteins. GSEA attributes a specific weight to each gene/protein in the input list that depends on a metric of choice, which is usually represented by quantitative expression data. However, expression data are not always available. Here, GSEA based on betweenness centrality of a protein-protein interaction (PPI) network is described and applied to two cases, where an expression metric is missing. First, personalized PPI networks were generated from genes displaying alterations (assessed by array comparative genomic hybridization and whole exome sequencing) in four probands bearing a 16p13.11 microdeletion in common and several other point variants. Patients showed disease phenotypes linked to neurodevelopment. All networks were assembled around a cluster of first interactors of altered genes with high betweenness centrality. All four clusters included genes known to be involved in neurodevelopmental disorders with different centrality. Moreover, the GSEA results pointed out to the evidence of "cell cycle" among enriched pathways. Second, a large interaction network obtained by merging proteomics studies on three neurodegenerative disorders was analyzed from the topological point of view. We observed that most central proteins are often linked to Parkinson's disease. The selection of these proteins improved the specificity of GSEA, with "Metabolism of amino acids and derivatives" and "Cellular response to stress or external stimuli" as top-ranked enriched pathways. In conclusion, betweenness centrality revealed to be a suitable metric for GSEA. Thus, centrality-based GSEA represents an opportunity for precision medicine and network medicine.

Indexed as

gene set enrichment analysisnetwork medicineneurodegenerationneurodevelopmentsystems medicinetopological analysis

Identifiers

PMID33719329
PMCPMC7943873

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.